We describe a method to jointly pre-train 
speech and text in an encoder-decoder modeling framework for speech translation and recognition. 
The proposed method incorporates 
four self-supervised and supervised subtasks for cross modality learning. 
A self-supervised speech subtask leverages unlabelled speech data, and a (self-)supervised text to text subtask makes use of 
abundant text training data. 
Two auxiliary supervised speech tasks are included to unify speech and text modeling space. 
Our contribution lies in integrating linguistic information from the text corpus into the speech pre-training.
Detailed analysis reveals learning interference among subtasks. 
Two pre-training configurations for speech translation and recognition, respectively, are presented to alleviate subtask interference.
Our experiments show the proposed method can effectively fuse speech and text information into one model. 
It achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset and 
comparable WERs to wav2vec 2.0 on the Librispeech speech recognition task.

Unified Speech-Text Pre-training for Speech Translation and Recognition

We present a simple yet effective approach to build multilingual speech-to-text (ST) translation through efficient transfer learning from a pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and cross-modality transfer ability by only finetuning 10~50% of the pretrained parameters. This effectively leverages large pretrained models at low training cost such as wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation. This sets a new state-of-the-art for 36 translation directions (and surpassing cascaded ST for 26 of them) on the large-scale multilingual ST benchmark CoVoST 2 (+6.4 BLEU on average for En-X directions and +6.7 BLEU for X-En directions). Our approach demonstrates strong zero-shot performance in a many-to-many multilingual model (+5.6 BLEU on average across 28 non-English directions), making it an appealing approach for attaining high-quality speech translation with improved parameter and data efficiency.

Multilingual Speech Translation from Efficient Finetuning of Pretrained Models

Pretraining and multitask learning are widely used to improve the speech translation performance. In this study, we are interested in training a speech translation model along with an auxiliary text translation task. We conduct a detailed analysis to understand the impact of the auxiliary task on the primary task within the multitask learning framework. Our analysis confirms that multitask learning tends to generate similar decoder representations from different modalities and preserve more information from the pretrained text translation modules. We observe minimal negative transfer effect between the two tasks and sharing more parameters is helpful to transfer knowledge from the text task to the speech task. The analysis also reveals that the modality representation difference at the top decoder layers is still not negligible, and those layers are critical for the translation quality. Inspired by these findings, we propose three methods to improve translation quality. First, a parameter sharing and initialization strategy is proposed to enhance information sharing between the tasks. Second, a novel attention-based regularization is proposed for the encoders and pulls the representations from different modalities closer. Third, an online knowledge distillation is proposed to enhance the knowledge transfer from the text to the speech task. Our experiments show that the proposed approach improves translation performance by more than 2 BLEU over a strong baseline and achieves state-of-the-art results on the MuST-C English-German, English-French and English-Spanish language pairs.

Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task

Posters: Language Grounding, Speech and Multimodality

poster

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3D: Machine Translation and Multilinguality #2

technical paper

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IJCNLP-AACL 2021

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Yun Tang

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Unified Speech-Text Pre-training for Speech Translation and Recognition

Multilingual Speech Translation from Efficient Finetuning of Pretrained Models

Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task

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